2 deleted 5 characters in body
source | link

So far what you are proposing sounds like a reasonable approach. However, I don't think you will know how well it would workworks until you try it, just like you have tried SIFT.

I have a question though. Why are you restricting yourself to DCT? There are lots of representations that have been used for texture classification: co-occurrence matrices, local binary patterns, etc. The fact that you have only found one paper on using DCT for texture classification would suggest that this is not the most commonly used feature for this problem. I would recommend that you widen your literature search to see what other features people have used, and how well they have worked.

So far what you are proposing sounds like a reasonable approach. However, I don't think you will know how well it would work until you try it, just like you have tried SIFT.

I have a question though. Why are you restricting yourself to DCT? There are lots of representations that have been used for texture classification: co-occurrence matrices, local binary patterns, etc. The fact that you have only found one paper on using DCT for texture classification would suggest that this is not the most commonly used feature for this problem. I would recommend that you widen your literature search to see what other features people have used, and how well they have worked.

So far what you are proposing sounds like a reasonable approach. However, I don't think you will know how well it works until you try it, just like you have tried SIFT.

I have a question though. Why are you restricting yourself to DCT? There are lots of representations that have been used for texture classification: co-occurrence matrices, local binary patterns, etc. The fact that you have only found one paper on using DCT for texture classification would suggest that this is not the most commonly used feature for this problem. I would recommend that you widen your literature search to see what other features people have used, and how well they have worked.

1
source | link

So far what you are proposing sounds like a reasonable approach. However, I don't think you will know how well it would work until you try it, just like you have tried SIFT.

I have a question though. Why are you restricting yourself to DCT? There are lots of representations that have been used for texture classification: co-occurrence matrices, local binary patterns, etc. The fact that you have only found one paper on using DCT for texture classification would suggest that this is not the most commonly used feature for this problem. I would recommend that you widen your literature search to see what other features people have used, and how well they have worked.